To examine prevalence and risk factors associated with primary dysmenorrhea among Chinese female university students in Hunan province, China. A cross-sectional study. Female university students in Changsha city, Hunan province, China. Four thousand six hundred six female individuals selected using a multistage cluster random sampling method. A self-administered questionnaire, which included sociodemographic information, lifestyle habits, emotional characteristics, and menstruation pattern, was used to collect data. Information about menarche, body mass index, cycle length and regularity, primary dysmenorrhea, physical exercise, depression, and anxiety. The prevalence of primary dysmenorrhea was 41.7% (1921/4606) among Chinese female university students. Multivariate binary logistic regression indicated that being a minority (odds ratio [OR], 1.335; 95% confidence interval [CI], 1.083-1.646), underweight (OR, 1.249; 95% CI, 1.08-1.42), annual household income less than 80,000 CNY (OR, 1.169; 95% CI, 1.018-1.342), maternal history of dysmenorrhea (OR, 2.553; 95% CI, 2.236-2.915), age at menarche younger than 12 years (OR, 1.161; 95% CI, 1.013-1.329), irregular menstrual cycle (OR, 1.216; 95% CI, 1.063-1.391), and skipping breakfast (OR, 1.294; 95% CI, 1.124-1.490) were associated risk factors of primary dysmenorrhea. The prevalence of primary dysmenorrhea among Chinese university students is relatively high. Various risk factors were identified that associated with primary dysmenorrhea. More effort and attention should therefore be directed toward this health problem in China.

Background: The world is experiencing an unprecedented challenge due to the coronavirus disease (COVID-19) pandemic. However, it is unclear whether people’s lifestyles will change as a result.Objective: The aim of this study is to explore perceived lifestyle changes after the outbreak of COVID-19 and their association with subjective well-being (SWB) among the general population in Mainland China.Methods: An online survey was conducted in May 2020. Lifestyle behaviors including leisure-time physical exercise, leisure-time screen time, and dietary intake were self-reported. SWB was measured using the General Wellbeing Schedule (GWS). Other covariates including sociodemographic factors, self-rated physical health, perceived social support, and loneliness were also assessed by a structured questionnaire. A multivariate ordinal regression method was used to analyze the association between SWB and lifestyle behaviors as well as perceived lifestyle changes.Results: A total of 1033 participants aged between 18 and 60 years were included in this study. The mean GWS score was 71.7 points. About 70% of the respondents reported spending more time looking at screens, whereas about 30% reported an increased frequency of vegetable and fruit intake after the outbreak of COVID-19. Inactive physical exercise (odds ratio [OR] 1.16, 95% CI 1.02-1.48), infrequent vegetable intake (OR 1.45, 95% CI 1.10-1.90), infrequent fruit intake (OR 1.31, 95% CI 1.01-1.70), and often skipping breakfast (OR 1.43, 95% CI 1.08-1.91) were associated with lower SWB after adjusting for sociodemographic factors, self-rated physical health, perceived social support, and loneliness. Moreover, participants who perceived a decrease in the frequency of vegetable, fruit, and breakfast intake were more likely to report lower SWB.Conclusions: The COVID-19 pandemic may have positive and negative impacts on different aspects of lifestyle behaviors. Both unhealthy lifestyle behaviors and negative lifestyle changes were associated with lower SWB. These findings provide scientific evidence that can inform lifestyle guidelines and public mental health interventions during the COVID-19 outbreak.

Sleep problems have become the most common complaints among the elderly. There are a few studies that explored the prevalence of poor sleep quality and its associated factors among the elderly in nursing homes. Therefore, this study aimed to examine the prevalence of poor sleep quality and its associated factors among the Chinese elderly in nursing homes. A total of 817 elderly residents, from 24 nursing homes, were included in this cross-sectional study. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and poor sleep quality was defined as PSQI >5. Multiple binary logistic regression was used to estimate the strength of the association between risk factors and poor sleep quality in terms of adjusted odds ratios (AORs) and their 95% confidence intervals (CIs), and interactions of risk factors for poor sleep quality were also examined. The prevalence of poor sleep quality was 67.3% (95% CI: 64.0, 70.5%) among the Chinese elderly in nursing homes. Multiple binary logistic regression results showed that participants with the following characteristics had an increased risk of poor sleep quality after adjustments for other confounders: being 70–79 years old (AOR: 1.78, 95% CI: 1.08, 2.92) or 80 years old and above (AOR: 2.67, 95% CI: 1.68, 4.24); having one to two kinds of chronic diseases (AOR: 2.05, 95% CI: 1.39, 3.01) or three or more kinds of chronic diseases (AOR: 2.35, 95% CI: 1.39, 4.00); depression symptoms (AOR: 1.08, 95% CI: 1.04, 1.11), anxiety symptoms (AOR: 1.11, 95% CI: 1.05, 1.18), and social support(AOR: 0.97, 95% CI: 0.95, 0.99). Additive interactions were detected between age and anxiety symptoms (AOR: 8.34, 95% CI: 4.43, 15.69); between chronic disease and anxiety symptoms (AOR: 8.61, 95% CI; 4.28, 17.31); and between social support and anxiety symptoms (AOR: 6.43, 95% CI: 3.22, 12.86). The prevalence of poor sleep quality among the elderly in nursing homes is relatively high. Besides, anxiety symptoms has additive interactions with age, chronic disease and social support for poor sleep quality. These findings have significant implications for interventions that aim to improve sleep quality among elderly residents in nursing homes.

Objectives To examine the association between the prevalence of poor sleep quality and depression symptoms among the elderly in the nursing homes of Hunan province in China. Design, Setting and participants This was a cross-sectional study investigating 817 elderly people from 24 nursing homes in China’s Hunan province. Main outcome measures Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) such that poor sleep quality was defined as PSQI Score >5. In addition, depression symptoms were assessed using the Geriatric Depression Scale (GDS). Linear regression models and binary logistic regression models were used to analyse the relationship between the prevalence of poor sleep quality and depression symptoms. Results The mean PSQI Score was 8.5±4.9, and the prevalence of poor sleep quality was 67.3%. Additionally, the mean GDS Score was 9.8±7.5, and the prevalence of depression symptoms was 36.0%. Elderly people with poor sleep quality had increased GDS Score (mean difference=2.54, 95% CI 1.66 to 3.42) and increased risk of depression symptoms (OR=3.19, 95% CI 2.04 to 4.98) after controlling for demographics, chronic disease history, lifestyle behaviours, social support, activities of daily living and negative life events. Conclusions The prevalence of poor sleep quality was relatively high, and this was associated with increased depression symptoms. Therefore, poor sleep quality could be speculated as a marker of current depression symptoms in the elderly.